Glioblastoma Multiforme (GBM) is a very aggressive brain tumour. Previous studies have suggested that the morphological distribution of single GBM cells may hold information about the severity. This study aims to find if there is a potential for automated morphological qualification and clustering of GBM cells and what it shows. In this context, phase-contrast images from 10 different GBMcell cultures were analyzed. To test the hypothesis that morphological differences exist between the cell cultures, images of single GBM cells images were created from an image over the well using CellProfiler and Python. Singlecellimages were passed through multiple different feature extraction models to identify the model showing the most promise for this dataset. The features were then clustered and quantified to see if any differentiation exists between the cell cultures. The results suggest morphological feature differences exist between GBM cell cultures when using automated models. The siamese network managed to construct clusters of cells having very similar morphology. I conclude that the 10 cell cultures seem to have cells with morphological differences. This highlights the importance of future studies to find what these morphological differences imply for the patients' survivability and choice of treatment.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第21429号 / 農博第2307号 / 学位論文||H30||N5157(農学部図書室) / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 近藤 直, 准教授 小川 雄一, 教授 飯田 訓久 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

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Buildings are essential parts to human life, which provide the place to dwell, educate, entertain, etc. However, they are usually vulnerable to earthquakes, and collapsed buildings are the main factor of fatalities and directly impact livelihoods. It is particularly important to quickly and accurately obtain damaged building conditions for further planning rescue. Remote sensing has the ability to quickly capture the information of damaged buildings in a large area, and remote sensing imagery has been used by government organizations, international agencies, and insurance industries for assessing post-event damage. The application of deep learning is encouraged by recent technological developments, enabling the processing of increasing amounts of data in a reasonable time as well as the use of more complex models. In this thesis, deep learning is explored for identifying collapsed buildings using very high-resolution remote sensing imagery after the 2010 Haiti earthquake. In the present study, a simple architecture of convolutional neural network (CNN) model was proposed to evaluate the potential of CNN for extracting features and detecting collapsed buildings using only post-event very high-resolution remote sensing imagery. Three balancing methods were considered to reduce the effect of the imbalance problem for the performance of the CNN, and the results showed that a suitable balancing method should be considered when facing imbalance dataset to retrieve the distribution of collapsed buildings. To improve the classification accuracy, pre- and post-event very high-resolution remote sensing imagery were considered, and a conventional classification method was combined with the CNN. Compared to conventional texture features, deep features learnt from CNNs had better performance for identifying collapsed buildings, and the accuracy was further improved by combing CNN features with random forest classifier. For the limited dataset, a pretrained CNN model was applied to detect collapsed buildings, and the effect of data augmentation was also investigated. The experimental results demonstrated that the pretrained CNN model outperformed the model trained from scratch for identifying collapsed buildings.